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2020-02-02 12:28:25 Iteration 1250 Training Loss: 1.914e-01 Loss in Target Net: 6.772e-02
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2020-02-02 12:28:42 Iteration 1300 Training Loss: 1.832e-01 Loss in Target Net: 5.866e-02
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2020-02-02 12:28:59 Iteration 1350 Training Loss: 1.930e-01 Loss in Target Net: 6.322e-02
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2020-02-02 12:29:17 Iteration 1400 Training Loss: 1.935e-01 Loss in Target Net: 6.599e-02
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2020-02-02 12:29:34 Iteration 1450 Training Loss: 1.888e-01 Loss in Target Net: 6.399e-02
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2020-02-02 12:29:51 Iteration 1499 Training Loss: 1.878e-01 Loss in Target Net: 5.835e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:30:00, Epoch 0, Iteration 7, loss 0.720 (0.483), acc 86.538 (88.800)
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2020-02-02 12:30:58, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-3.3397734, -1.1816226, -1.6427957, 1.6357713, -1.5311726, 0.083695084, 4.0923824, -1.8032396, 6.655831, -2.557654], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:31:58 Epoch 59, Val iteration 0, acc 93.200 (93.200)
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2020-02-02 12:32:06 Epoch 59, Val iteration 19, acc 92.600 (92.520)
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* Prec: 92.52000160217285
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 32
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DPN92 1
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=33, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/33
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:22:08 Iteration 0 Training Loss: 1.025e+00 Loss in Target Net: 1.400e+00
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2020-02-02 12:22:27 Iteration 50 Training Loss: 2.548e-01 Loss in Target Net: 7.228e-02
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2020-02-02 12:22:45 Iteration 100 Training Loss: 2.246e-01 Loss in Target Net: 4.702e-02
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2020-02-02 12:23:03 Iteration 150 Training Loss: 2.112e-01 Loss in Target Net: 4.289e-02
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2020-02-02 12:23:20 Iteration 200 Training Loss: 2.069e-01 Loss in Target Net: 2.815e-02
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2020-02-02 12:23:39 Iteration 250 Training Loss: 2.022e-01 Loss in Target Net: 2.206e-02
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2020-02-02 12:23:59 Iteration 300 Training Loss: 1.996e-01 Loss in Target Net: 1.999e-02
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2020-02-02 12:24:18 Iteration 350 Training Loss: 1.917e-01 Loss in Target Net: 2.699e-02
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2020-02-02 12:24:38 Iteration 400 Training Loss: 1.935e-01 Loss in Target Net: 2.525e-02
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2020-02-02 12:24:57 Iteration 450 Training Loss: 1.952e-01 Loss in Target Net: 2.147e-02
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2020-02-02 12:25:15 Iteration 500 Training Loss: 1.909e-01 Loss in Target Net: 2.590e-02
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2020-02-02 12:25:34 Iteration 550 Training Loss: 1.865e-01 Loss in Target Net: 2.487e-02
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2020-02-02 12:25:50 Iteration 600 Training Loss: 1.900e-01 Loss in Target Net: 3.310e-02
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2020-02-02 12:26:06 Iteration 650 Training Loss: 1.828e-01 Loss in Target Net: 3.214e-02
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2020-02-02 12:26:25 Iteration 700 Training Loss: 1.835e-01 Loss in Target Net: 3.452e-02
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2020-02-02 12:26:42 Iteration 750 Training Loss: 1.840e-01 Loss in Target Net: 3.451e-02
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2020-02-02 12:26:59 Iteration 800 Training Loss: 1.879e-01 Loss in Target Net: 3.859e-02
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2020-02-02 12:27:17 Iteration 850 Training Loss: 1.851e-01 Loss in Target Net: 2.629e-02
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2020-02-02 12:27:35 Iteration 900 Training Loss: 1.866e-01 Loss in Target Net: 3.439e-02
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2020-02-02 12:27:52 Iteration 950 Training Loss: 1.831e-01 Loss in Target Net: 3.006e-02
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2020-02-02 12:28:09 Iteration 1000 Training Loss: 1.860e-01 Loss in Target Net: 3.033e-02
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2020-02-02 12:28:27 Iteration 1050 Training Loss: 1.846e-01 Loss in Target Net: 2.884e-02
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2020-02-02 12:28:45 Iteration 1100 Training Loss: 1.822e-01 Loss in Target Net: 3.191e-02
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2020-02-02 12:29:01 Iteration 1150 Training Loss: 1.807e-01 Loss in Target Net: 3.674e-02
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2020-02-02 12:29:18 Iteration 1200 Training Loss: 1.792e-01 Loss in Target Net: 2.787e-02
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2020-02-02 12:29:36 Iteration 1250 Training Loss: 1.799e-01 Loss in Target Net: 2.875e-02
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2020-02-02 12:29:54 Iteration 1300 Training Loss: 1.794e-01 Loss in Target Net: 2.531e-02
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2020-02-02 12:30:12 Iteration 1350 Training Loss: 1.822e-01 Loss in Target Net: 2.369e-02
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2020-02-02 12:30:28 Iteration 1400 Training Loss: 1.796e-01 Loss in Target Net: 2.714e-02
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2020-02-02 12:30:45 Iteration 1450 Training Loss: 1.798e-01 Loss in Target Net: 2.608e-02
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2020-02-02 12:31:03 Iteration 1499 Training Loss: 1.834e-01 Loss in Target Net: 2.616e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 12:31:12, Epoch 0, Iteration 7, loss 0.304 (0.462), acc 94.231 (89.800)
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2020-02-02 12:32:10, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.0938239, -1.3297225, 0.8009595, 0.64525056, -3.3501985, -3.7383437, 7.6108704, -3.8410916, 6.864364, -2.1510859], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:33:10 Epoch 59, Val iteration 0, acc 93.000 (93.000)
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2020-02-02 12:33:17 Epoch 59, Val iteration 19, acc 92.600 (93.250)
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* Prec: 93.2500015258789
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 33
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DPN92 0
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=33, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/33
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-03 05:55:17 Iteration 0 Training Loss: 1.019e+00 Loss in Target Net: 1.345e+00
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2020-02-03 05:55:48 Iteration 50 Training Loss: 2.523e-01 Loss in Target Net: 6.572e-02
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2020-02-03 05:56:37 Iteration 100 Training Loss: 2.260e-01 Loss in Target Net: 4.375e-02
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2020-02-03 05:57:21 Iteration 150 Training Loss: 2.138e-01 Loss in Target Net: 3.811e-02
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2020-02-03 05:58:06 Iteration 200 Training Loss: 2.014e-01 Loss in Target Net: 4.490e-02
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2020-02-03 05:58:57 Iteration 250 Training Loss: 1.977e-01 Loss in Target Net: 3.599e-02
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2020-02-03 05:59:18 Iteration 300 Training Loss: 1.980e-01 Loss in Target Net: 3.634e-02
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2020-02-03 05:59:35 Iteration 350 Training Loss: 1.942e-01 Loss in Target Net: 3.845e-02
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2020-02-03 05:59:53 Iteration 400 Training Loss: 1.942e-01 Loss in Target Net: 4.612e-02
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2020-02-03 06:00:11 Iteration 450 Training Loss: 1.898e-01 Loss in Target Net: 3.335e-02
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2020-02-03 06:00:27 Iteration 500 Training Loss: 1.893e-01 Loss in Target Net: 3.304e-02
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2020-02-03 06:00:45 Iteration 550 Training Loss: 1.877e-01 Loss in Target Net: 3.525e-02
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2020-02-03 06:01:02 Iteration 600 Training Loss: 1.918e-01 Loss in Target Net: 3.418e-02
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2020-02-03 06:01:19 Iteration 650 Training Loss: 1.822e-01 Loss in Target Net: 2.877e-02
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2020-02-03 06:01:38 Iteration 700 Training Loss: 1.809e-01 Loss in Target Net: 2.996e-02
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2020-02-03 06:01:56 Iteration 750 Training Loss: 1.853e-01 Loss in Target Net: 3.591e-02
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2020-02-03 06:02:14 Iteration 800 Training Loss: 1.851e-01 Loss in Target Net: 3.191e-02
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2020-02-03 06:02:32 Iteration 850 Training Loss: 1.851e-01 Loss in Target Net: 3.713e-02
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2020-02-03 06:02:50 Iteration 900 Training Loss: 1.806e-01 Loss in Target Net: 4.408e-02
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2020-02-03 06:03:09 Iteration 950 Training Loss: 1.832e-01 Loss in Target Net: 2.909e-02
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2020-02-03 06:03:26 Iteration 1000 Training Loss: 1.854e-01 Loss in Target Net: 3.130e-02
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2020-02-03 06:03:43 Iteration 1050 Training Loss: 1.817e-01 Loss in Target Net: 4.414e-02
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2020-02-03 06:04:00 Iteration 1100 Training Loss: 1.860e-01 Loss in Target Net: 4.121e-02
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2020-02-03 06:04:18 Iteration 1150 Training Loss: 1.823e-01 Loss in Target Net: 3.428e-02
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2020-02-03 06:04:36 Iteration 1200 Training Loss: 1.791e-01 Loss in Target Net: 4.228e-02
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2020-02-03 06:04:54 Iteration 1250 Training Loss: 1.833e-01 Loss in Target Net: 4.594e-02
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2020-02-03 06:05:12 Iteration 1300 Training Loss: 1.795e-01 Loss in Target Net: 4.100e-02
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